• Contributed to the identification and development of business-relevant data science and machine learning use cases, collaborating with cross-functional teams to ensure alignment with organizational goals.•Built and implemented end-to-end machine learning projects, leveraging inline process parameters and measurement equipment, providing data-driven insights for process and product analysis.•Developed and optimized machine learning models, refining data pipelines to ensure robust data flow and support for model training and evaluation.

•Conducted in-depth experiments of different statistical and neural network-based models for keyword extraction and benchmarked them on public datasets•Optimized relevance and diversity of results by combinig TF-IDF and KeyBERT.•Engineered a microservices architecture for "Kiwrd," containerizing the different micro services which significantly improved scalability, resulted in low-latency (20% faster compared to a monolithic architecture) and enhanced fault tolerance.

•Discovered meaningful correlations between various store performance indicators, such as sales volume and customer satisfaction.•Utilized statistical techniques to elucidate how specific factors, such as store location or product placement, influenced the overall performance of each shop•Developed a comprehensive sales insights dashboard using PowerBI and Django for a web alternative, providing shop owners with an accessible and intuitive tool for data-driven decision-making.

Developped a dashbaord to offer descriptive analytics on employee's timesheet data from provided excel input files